

import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase_			      =      '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_			      =      direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_			      =      re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_			      =      re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase_			      =      re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase_			      =      [
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,						SCREAMING_SNAKE_CASE__			)
						return [m.group(0			) for m in matches]


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_  =      {
						    config.replace('''Config'''    ,						''''''			): model_type for model_type, config in config_maping_names.items()
						}

						# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
						snake_case_  =      collections.defaultdict(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      collections.defaultdict(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      collections.defaultdict(SCREAMING_SNAKE_CASE__			)

						# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
						for attr_name in dir(SCREAMING_SNAKE_CASE__			):
												snake_case_  =      None
												if _re_tf_models.match(SCREAMING_SNAKE_CASE__			) is not None:
																		snake_case_  =      tf_models
																		snake_case_  =      _re_tf_models.match(SCREAMING_SNAKE_CASE__			).groups()[0]
												elif _re_flax_models.match(SCREAMING_SNAKE_CASE__			) is not None:
																		snake_case_  =      flax_models
																		snake_case_  =      _re_flax_models.match(SCREAMING_SNAKE_CASE__			).groups()[0]
												elif _re_pt_models.match(SCREAMING_SNAKE_CASE__			) is not None:
																		snake_case_  =      pt_models
																		snake_case_  =      _re_pt_models.match(SCREAMING_SNAKE_CASE__			).groups()[0]

												if lookup_dict is not None:
																		while len(SCREAMING_SNAKE_CASE__			) > 0:
																								if attr_name in model_prefix_to_model_type:
																														snake_case_  =      True
																														break
																								# Try again after removing the last word in the name
																								snake_case_  =      ''''''.join(camel_case_split(SCREAMING_SNAKE_CASE__			)[:-1]			)

						snake_case_  =      set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
						snake_case_  =      list(SCREAMING_SNAKE_CASE__			)
						all_models.sort()

						snake_case_  =      {'''model_type''': all_models}
						snake_case_  =      [pt_models[t] for t in all_models]
						snake_case_  =      [tf_models[t] for t in all_models]
						snake_case_  =      [flax_models[t] for t in all_models]

						# Now let's use the auto-mapping names to make sure
						snake_case_  =      {}
						for t in all_models:
												if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																		snake_case_  =      '''AutoProcessor'''
												elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																		snake_case_  =      '''AutoTokenizer'''
												elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																		snake_case_  =      '''AutoFeatureExtractor'''
												else:
																		# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																		snake_case_  =      '''AutoTokenizer'''

						snake_case_  =      [processors[t] for t in all_models]

						return pd.DataFrame(SCREAMING_SNAKE_CASE__			)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      [
						    transformers_module.models.auto.modeling_auto,
						    transformers_module.models.auto.modeling_tf_auto,
						    transformers_module.models.auto.modeling_flax_auto,
						]
						for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
												snake_case_  =      [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
												snake_case_  =      [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
												# Loop through all three frameworks
												for module, cls, mapping in zip(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																		# The type of pipeline may not exist in this framework
																		if not hasattr(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																								continue
																		# First extract all model_names
																		snake_case_  =      []
																		for name in getattr(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			).values():
																								if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																														model_names.append(SCREAMING_SNAKE_CASE__			)
																								else:
																														model_names.extend(list(SCREAMING_SNAKE_CASE__			)			)

            # Add pipeline tag and auto model class for those models
																		table.update({model_name: (pipeline_tag, cls) for model_name in model_names}			)

						return table


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      get_frameworks_table()
						snake_case_  =      Dataset.from_pandas(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      hf_hub_download(
						    '''huggingface/transformers-metadata'''    ,						'''pipeline_tags.json'''    ,						repo_type='''dataset'''    ,						token=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      Dataset.from_json(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      {
						    tags_dataset[i]['''model_class''']: (tags_dataset[i]['''pipeline_tag'''], tags_dataset[i]['''auto_class'''])
						    for i in range(len(SCREAMING_SNAKE_CASE__			)			)
						}
						snake_case_  =      update_pipeline_and_auto_class_table(SCREAMING_SNAKE_CASE__			)

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						snake_case_  =      sorted(table.keys()			)
						snake_case_  =      pd.DataFrame(
						    {
						        '''model_class''': model_classes,
						        '''pipeline_tag''': [table[m][0] for m in model_classes],
						        '''auto_class''': [table[m][1] for m in model_classes],
						    }			)
						snake_case_  =      Dataset.from_pandas(SCREAMING_SNAKE_CASE__			)

						with tempfile.TemporaryDirectory() as tmp_dir:
												frameworks_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE__    ,						'''frameworks.json'''			)			)
												tags_dataset.to_json(os.path.join(SCREAMING_SNAKE_CASE__    ,						'''pipeline_tags.json'''			)			)

												if commit_sha is not None:
																		snake_case_  =      (
																		    F'''Update with commit {commit_sha}\n\nSee: '''
																		    F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
																		)
												else:
																		snake_case_  =      '''Update'''

												upload_folder(
												    repo_id='''huggingface/transformers-metadata'''    ,						folder_path=SCREAMING_SNAKE_CASE__    ,						repo_type='''dataset'''    ,						token=SCREAMING_SNAKE_CASE__    ,						commit_message=SCREAMING_SNAKE_CASE__    ,						)


def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
						snake_case_  =      transformers_module.pipelines.SUPPORTED_TASKS
						snake_case_  =      []
						for key in pipeline_tasks:
												if key not in in_table:
																		snake_case_  =      pipeline_tasks[key]['''pt''']
																		if isinstance(SCREAMING_SNAKE_CASE__    ,						(list, tuple)			):
																								snake_case_  =      model[0]
																		snake_case_  =      model.__name__
																		if model not in in_table.values():
																								missing.append(SCREAMING_SNAKE_CASE__			)

						if len(SCREAMING_SNAKE_CASE__			) > 0:
												snake_case_  =      ''', '''.join(SCREAMING_SNAKE_CASE__			)
												raise ValueError(
												    '''The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '''
												    F'''`utils/update_metadata.py`: {msg}. Please add them!'''			)


if __name__ == "__main__":
					lowerCAmelCase_			      =      argparse.ArgumentParser()
					parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
					parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
					parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
					lowerCAmelCase_			      =      parser.parse_args()

					if args.check_only:
										check_pipeline_tags()
					else:
										update_metadata(args.token, args.commit_sha)






"""simple docstring"""





def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			""""""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[chr(i + 65     ) for i in range(26     )]
      # Remove duplicate characters from key
      lowerCAmelCase    		=			remove_duplicates(key.upper()     )
      lowerCAmelCase    		=			len(SCREAMING_SNAKE_CASE     )
      # First fill cipher with key characters
      lowerCAmelCase    		=			{alphabet[i]: char for i, char in enumerate(SCREAMING_SNAKE_CASE     )}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(SCREAMING_SNAKE_CASE     )				,		26     ):
            lowerCAmelCase    		=			alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  lowerCAmelCase    		=			alphabet[i - offset]
            lowerCAmelCase    		=			char
      return cipher_alphabet


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		dict[str, str]     ):







      '''simple docstring'''




      return "".join(cipher_map.get(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) for ch in message.upper()     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		dict[str, str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			{v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) for ch in message.upper()     )


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			input("""Enter message to encode or decode: """     ).strip()
      lowerCAmelCase    		=			input("""Enter keyword: """     ).strip()
      lowerCAmelCase    		=			input("""Encipher or decipher? E/D:"""     ).strip()[0].lower()
      try:
            lowerCAmelCase    		=			{"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""     )
      lowerCAmelCase    		=			create_cipher_map(SCREAMING_SNAKE_CASE     )
      print(func(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()






